Tracking the Intangible: Quantifying Effort in NFL Running Backs
Image source: The Tower
Introduction
Effort is a fundamental and yet often invisible and subjective dimension of athletic performance. In American Football, one of the most-watched and popular sports in the U.S., known for its quick decision-making, complex tactics, and athletically demanding displays of strength, endurance, and speed, traditional statistics like yards after contact or expected points added tend to focus on outcomes rather than the process behind them. This raises important questions: What does it take to gain a yard? Is a player exceeding their capabilities, therefore, making an effort? If so, how often or how close does a player come to their “best”?
These questions are at the heart of our project. While previous research in professional soccer has used theoretical maximum acceleration as a proxy for effort, no such framework currently exists in the NFL. Our goal is to address that gap by developing a player-specific, data-driven metric for measuring effort among running backs (RBs). Specifically, we focus on physical effort as expressed through raw acceleration and speed, which are variables we can observe directly in player tracking data.
Effort, of course, is a complex, intangible, and most often subjective concept, influenced by mental and emotional state of a player as well as tactical and game factors, including player position, opposing defense, snap count or play volume to play call/assignment, and other game context. However, given the data available, we concentrate on one measurable facet: a player’s movement relative to his individual maximum capabilities. Using statistical models, we estimate each RB’s personal performance ceiling and assess how frequently and how closely they approach it during games.
By quantifying effort, we hope to provide not only to teams and sport data analysts, but also fans with deeper insights into how much football players are truly contributing on the field — even if they’re not reflected in conventional stats or the outcome of a game. In doing so, we also aim to contribute to a growing body of research around quantifying effort since most research focuses on win probabilities and other outcomes at the game level rather than at the individual level.
Data
The data used for this project were the games, plays, players, tracking data sets from the NFL Big Data Bowl 2025, weeks 1-9 on Kaggle (NFL Big Data Bowl 2025).
Data pre-porcessing
- We limited our dataset to NFL running backs with more than 20 rushes per play during the 2022 season.
- We limited the rows to running plays where a running back (RB) is the ball carrier.
- We Trimmed each play to frames between handoff and at end of a play.
Methods
In this project, we define effort as a running back’s ability to consistently reach a combination of their individual maximal acceleration for any given speed.
Metric #1: Linear Regression
Our main methodological approach to addressing this question centered around previous research that has explored soccer players reaching their theoretical max acceleration capacity for every running speed (Morin et al. 2021). Similarly, we adopted this framework by plotting an individual running back’s profile based on the maximal acceleration the running back could generate for every possible running speed as follows:
- Within a speed interval ranging between 3 mph and the RB’s maximal speed, the two maximal acceleration values attained for each 0.2 mph sub-intervals were selected.
- A first linear regression was fit to these speed-acceleration points.
- Outlier points lying outside of 95% confidence interval around the linear function were removed.
- A linear regression was fit to the remaining points.
This effort metric was derived by transforming final regression line downward by 0.25 units.
Effort = percentage of points above the transformed regression line
side-by-side
plotly
Metric #2: Additive Quantile Regression Model
This metric quantifies how often a runnng back comes close to their maximal acceleration capacity.
Assumption of this effort metric: high acceleration and/or high speed movements are effortful.
For each running back, two regression models with adaptive spline bases were fitted to the 0.98 quantiles of positive and negative acceleration, respectively, both as functions of running speed. A vertical line at the 0.99 quantile of speed was also drawn.
This metric was derived by
[LATEX equation]
where di is the shortest distance from each point to its corresponding quantile regression line (either for positive or negative acceleration) or to the vertical line at the 0.99 quantile of speed.
- For points outside the quantile regression lines and/or the vertical line, the distance di was set to 0.
- For negative acceleration points, the effort score[change] was penalized with by a factor of 0.5 as deceleration is deemed less effortful.
Qgam
testing out a few players
Top Running backs
Top 10
linear regression
Linear quantile regression for acceleration(mph/s) vs speed(mph)
Results
[Describe your results. This can include tables and plots showing your results, as well as text describing how your models worked and the appropriate interpretations of the relevant output. (Note: Don’t just write out the textbook interpretations of all model coefficients. Instead, interpret the output that is relevant for your question of interest that is framed in the introduction)]]
scatterplot
Discussion
[Give your conclusions and summarize what you have learned with regards to your question of interest. Are there any limitations with the approaches you used? What do you think are the next steps to follow-up your project?]
- AS profile framework didn’t translate into football (maybe it works for other sports where players don’t get tackled but just run at hand-off)
- there are many dependencies in NFL
- this is a productive place to start quantifying effort
- further research should look into taking into account other factors/variables
Appendix
Trying a different interactive layout
Acknowledgements
- Used Dr. Ron Yurko and Quang Nguyen’s code to calculate distance from the nearest defender (Nguyen 2023)
References
Morin, Jean-Benoit, Yann Le Mat, Cristian Osgnach, Andrea Barnabò, Alessandro Pilati, Pierre Samozino, and Pietro E. di Prampero. 2021. “Individual Acceleration-Speed Profile in-Situ: A Proof of Concept in Professional Football Players.” Journal of Biomechanics 123: 110524. https://doi.org/https://doi.org/10.1016/j.jbiomech.2021.110524.
NFL Big Data Bowl. 2025. “NFL Big Data Bowl 2025 Dataset.” Kaggle. https://www.kaggle.com/competitions/nfl-big-data-bowl-2022/data.
Nguyen, Quang. 2023. “Turn-Angle.” https://github.com/qntkhvn/turn-angle/blob/main/scripts/01a_prep_rusher_data.R.